de Mello, F.L., Wilkinson, J.M. orcid.org/0000-0001-5577-3674 and Kadirkamanathan, V. orcid.org/0000-0002-4243-2501 (2021) Metaparametric neural networks for survival analysis. IEEE Transactions on Neural Networks and Learning Systems, 34 (8). pp. 4047-4056. ISSN 2162-237X
Abstract
Survival analysis is a critical tool for the modeling of time-to-event data, such as life expectancy after a cancer diagnosis or optimal maintenance scheduling for complex machinery. However, current neural network models provide an imperfect solution for survival analysis as they either restrict the shape of the target probability distribution or restrict the estimation to predetermined times. As a consequence, current survival neural networks lack the ability to estimate a generic function without prior knowledge of its structure. In this article, we present the metaparametric neural network framework that encompasses the existing survival analysis methods and enables their extension to solve the aforementioned issues. This framework allows survival neural networks to satisfy the same independence of generic function estimation from the underlying data structure that characterizes their regression and classification counterparts. Furthermore, we demonstrate the application of the metaparametric framework using both simulated and large real-world datasets and show that it outperforms the current state-of-the-art methods in: 1) capturing nonlinearities and 2) identifying temporal patterns, leading to more accurate overall estimations while placing no restrictions on the underlying function structure.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Hazards; Analytical models; Modeling; Estimation; Data models; Multi-layer neural network; Biological system modeling; Basis functions; hip replacement; metaparametric neural networks (MNNs); splines; survival analysis; time dependent |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) The University of Sheffield > Sheffield Teaching Hospitals |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Mar 2022 10:36 |
Last Modified: | 25 Jun 2024 11:09 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/tnnls.2021.3119510 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184971 |